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Data & AI

100 Days of Deep Learning in Hindi (CampusX)

Sikhami InstituteHindi

Free

Join for free
  • 84 video lectures (52h 12m)
  • Certificate of completion
  • Ask the teacher your doubts
  • Watch on the web and in the Android app

About this course

CampusX's deep learning course in Hindi goes from the perceptron to transformers, explaining the theory and building projects with Keras. Who it's for: learners who know machine learning basics and want to master deep learning, in Hindi. Taught in Hindi. These are the free, official videos of the "100 Days of Deep Learning" playlist from the CampusX YouTube channel (youtube.com/@campusx-official). All videos belong to CampusX; Sikhami is not affiliated with CampusX and simply arranges the public playlist into a course, so you can learn one lesson at a time and keep track of your progress.

What you will learn

• Perceptrons, forward propagation and backpropagation • Improving neural networks: dropout, regularisation, weight initialisation and batch normalisation • Optimisers such as momentum, NAG, Adam and RMSProp • Convolutional neural networks and image classification projects • Recurrent networks: RNN, LSTM and GRU • Attention, self-attention and the transformer architecture

Course content

1 section · 84 lectures · 52h 12m

  1. 1. 100 Days of Deep Learning

    • 100 Days of Deep Learning | Course Announcement18m
    • What is Deep Learning? Deep Learning Vs Machine Learning | Complete Deep Learning Course1h 6m
    • Types of Neural Networks | History of Deep Learning | Applications of Deep Learning33m
    • What is a Perceptron? Perceptron Vs Neuron | Perceptron Geometric Intuition38m
    • Perceptron Trick | How to train a Perceptron | Perceptron Part 2 | Deep Learning Full Course51m
    • Perceptron Loss Function | Hinge Loss | Binary Cross Entropy | Sigmoid Function59m
    • Problem with Perceptron7m
    • MLP Notation13m
    • Multi Layer Perceptron | MLP Intuition37m
    • Forward Propagation | How a neural network predicts output?15m
    • Customer Churn Prediction using ANN | Keras and Tensorflow | Deep Learning Classification35m
    • Handwritten Digit Classification using ANN | MNIST Dataset28m
    • Graduate Admission Prediction using ANN17m
    • Loss Functions in Deep Learning | Deep Learning | CampusX59m
    • Backpropagation in Deep Learning | Part 1 | The What?54m
    • Backpropagation Part 2 | The How | Complete Deep Learning Playlist59m
    • Backpropagation Part 3 | The Why | Complete Deep Learning Playlist40m
    • Vanishing Gradient Problem in ANN | Exploding Gradient Problem | Code Example32m
    • MLP Memoization | Complete Deep Learning Playlist25m
    • Gradient Descent in Neural Networks | Batch vs Stochastics vs Mini Batch Gradient Descent37m
    • How to Improve the Performance of a Neural Network30m
    • Early Stopping In Neural Networks | End to End Deep Learning Course12m
    • Data Scaling in Neural Network | Feature Scaling in ANN | End to End Deep Learning Course16m
    • Dropout Layer in Deep Learning | Dropouts in ANN | End to End Deep Learning27m
    • Dropout Layers in ANN | Code Example | Regression | Classification19m
    • Regularization in Deep Learning | L2 Regularization in ANN | L1 Regularization | Weight Decay in ANN35m
    • Activation Functions in Deep Learning | Sigmoid, Tanh and Relu Activation Function44m
    • Relu Variants Explained | Leaky Relu | Parametric Relu | Elu | Selu | Activation Functions Part 233m
    • Weight Initialization Techniques | What not to do? | Deep Learning49m
    • Xavier/Glorat And He Weight Initialization in Deep Learning21m
    • Batch Normalization in Deep Learning | Batch Learning in Keras43m
    • Optimizers in Deep Learning | Part 1 | Complete Deep Learning Course22m
    • Exponentially Weighted Moving Average or Exponential Weighted Average | Deep Learning18m
    • SGD with Momentum Explained in Detail with Animations | Optimizers in Deep Learning Part 238m
    • Nesterov Accelerated Gradient (NAG) Explained in Detail | Animations | Optimizers in Deep Learning27m
    • AdaGrad Explained in Detail with Animations | Optimizers in Deep Learning Part 426m
    • RMSProp Explained in Detail with Animations | Optimizers in Deep Learning Part 512m
    • Adam Optimizer Explained in Detail with Animations | Optimizers in Deep Learning Part 512m
    • Keras Tuner | Hyperparameter Tuning a Neural Network1h 5m
    • What is Convolutional Neural Network (CNN) | CNN Intution27m
    • CNN Vs Visual Cortex | The Famous Cat Experiment | History of CNN15m
    • CNN Part 3 | Convolution Operation29m
    • Padding & Strides in CNN | CNN Lecture 4 | Deep Learning24m
    • Pooling Layer in CNN | MaxPooling in Convolutional Neural Network27m
    • CNN Architecture | LeNet -5 Architecture20m
    • Comparing CNN Vs ANN | CampusX17m
    • Backpropagation in CNN | Part 1 | Deep Learning36m
    • CNN Backpropagation Part 2 | How Backpropagation works on Convolution, Maxpooling and Flatten Layers43m
    • Cat Vs Dog Image Classification Project | Deep Learning Project | CNN Project27m
    • Data Augmentation in Deep Learning | CNN26m
    • Pretrained models in CNN | ImageNET Dataset | ILSVRC | Keras Code24m
    • What does a CNN see? | Visualizing CNN Filters and Feature Maps | CampusX13m
    • What is Transfer Learning? Transfer Learning in Keras | Fine Tuning Vs Feature Extraction33m
    • Keras Functional Model | How to build non-linear Neural Networks?25m
    • Why RNNs are needed | RNNs Vs ANNs | RNN Part 130m
    • Recurrent Neural Network | Forward Propagation | Architecture41m
    • RNN Sentiment Analysis | RNN Code Example in Keras | CampusX36m
    • Types of RNN | Many to Many | One to Many | Many to One RNNs22m
    • How Backpropagation works in RNN | Backpropagation Through Time33m
    • Problems with RNN | 100 Days of Deep Learning32m
    • LSTM | Long Short Term Memory | Part 1 | The What? | CampusX42m
    • LSTM Architecture | Part 2 | The How? | CampusX1h 10m
    • LSTM | Part 3 | Next Word Predictor Using | CampusX1h 0m
    • Gated Recurrent Unit | Deep Learning | GRU | CampusX1h 26m
    • Deep RNNs | Stacked RNNs | Stacked LSTMs | Stacked GRUs | CampusX45m
    • Bidirectional RNN | BiLSTM | Bidirectional LSTM | Bidirectional GRU25m
    • The Epic History of Large Language Models (LLMs) | From LSTMs to ChatGPT | CampusX1h 27m
    • Encoder Decoder | Sequence-to-Sequence Architecture | Deep Learning | CampusX1h 13m
    • Attention Mechanism in 1 video | Seq2Seq Networks | Encoder Decoder Architecture41m
    • Bahdanau Attention Vs Luong Attention52m
    • Introduction to Transformers | Transformers Part 11h 0m
    • What is Self Attention | Transformers Part 2 | CampusX23m
    • Self Attention in Transformers | Deep Learning | Simple Explanation with Code!1h 23m
    • Scaled Dot Product Attention | Why do we scale Self Attention?50m
    • Self Attention Geometric Intuition | How to Visualize Self Attention | CampusX20m
    • Why is Self Attention called "Self"? | Self Attention Vs Luong Attention in Depth Lecture | CampusX22m
    • What is Multi-head Attention in Transformers | Multi-head Attention v Self Attention | Deep Learning38m
    • Positional Encoding in Transformers | Deep Learning | CampusX1h 13m
    • Layer Normalization in Transformers | Layer Norm Vs Batch Norm46m
    • Transformer Architecture | Part 1 Encoder Architecture | CampusX54m
    • Masked Self Attention | Masked Multi-head Attention in Transformer | Transformer Decoder1h 0m
    • Cross Attention in Transformers | 100 Days Of Deep Learning | CampusX34m
    • Transformer Decoder Architecture | Deep Learning | CampusX48m
    • Transformer Inference | How Inference is done in Transformer? | Deep Learning | CampusX45m